Effect of process variables on particle size and viability of<b><i>Bifidobacterium lactis</i></b>Bb-12 in genipin-gelatin microspheres
Bibliographic record
Abstract
Gelatin microspheres cross-linked with genipin were developed to encapsulate the probiotic Bifidobacterium lactis Bb-12 The effects of different gelatin concentrations (10-19% w/v), bloom strengths (175 and 300), surfactants, stirring rates during emulsion formation and genipin concentrations (0-10 mM) on the microsphere sizes and viability of bacterial cells were investigated. Principal Component Analysis revealed microsphere size distribution differed depending on the presence or absence of surfactants as well as a trend of increasing micropshere size with increasing gelatin concentration and bloom strength. Lower stirring rates resulted in larger microspheres with higher encapsulation yields of bifidobacteria Microsphere size and cell viability were not significantly (p < 0.05) influenced by increasing genipin concentrations up to 10 mM whereas microsphere stability in simulated gastric juice increased with increasing genipin concentration. The encapsulation yields were higher in 175 bloom strength gelatin microspheres than in 300. Cold-stage scanning electron microscopy showed encapsulated bacteria distributed throughout the genipin cross-linked gelatin matrix.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".